A Multimodal Human-Centered Framework for Assessing Pedestrian Well-Being in the Wild

Pedestrian well-being is a critical yet rarely measured component of sustainable urban mobility and livable city design. Existing approaches to evaluating pedestrian environments often rely on static, infrastructure-based indices or retrospective surveys, which overlook the dynamic, subjective, and psychophysiological dimensions of everyday walking experience. This paper introduces a multimodal, human-centered framework for assessing pedestrian well-being in the wild by integrating three complementary data streams: continuous physiological sensing, geospatial tracking, and momentary self-reports collected using the Experience Sampling Method. The framework conceptualizes pedestrian experience as a triangulation enabling a holistic understanding of how urban environments influence well-being. The utility of our framework is then demonstrated through a naturalistic case study conducted in the Greater Philadelphia region, in which participants wore research-grade wearable sensors and carried GPS-enabled smartphones during their regular daily activities. Physiological indicators of autonomic nervous system activity, including heart rate variability and electrodermal activity, were synchronized with spatial trajectories and in situ self-reports of stress, affect, and perceived infrastructure conditions. Results illustrate substantial inter- and intra-individual variability in both subjective experience and physiological response, as well as context-dependent patterns associated with traffic exposure, pedestrian infrastructure quality, and environmental enclosure. The findings also suggest that commonly used walkability indices may not fully capture experiential dimensions of pedestrian well-being. By enabling real-world, multimodal measurement of pedestrian experience, the proposed framework offers a scalable and transferable approach for advancing human-centered urban analytics.

Topic Context: Wearable or implantable systems that sense biological signals in real time.

Source: arXiv PDF: https://arxiv.org/pdf/2512.21200v1